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356 lines
13 KiB
356 lines
13 KiB
3 years ago
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import io
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import os
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import time
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from typing import Optional
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import pickle
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import numpy as np
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from numpy import float32
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import soundfile
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import paddle
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from yacs.config import CfgNode
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from paddlespeech.s2t.frontend.speech import SpeechSegment
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from paddlespeech.cli.asr.infer import ASRExecutor
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from paddlespeech.cli.log import logger
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from paddlespeech.cli.utils import MODEL_HOME
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.modules.ctc import CTCDecoder
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from paddlespeech.s2t.utils.utility import UpdateConfig
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from paddlespeech.server.engine.base_engine import BaseEngine
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from paddlespeech.server.utils.config import get_config
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from paddlespeech.server.utils.paddle_predictor import init_predictor
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from paddlespeech.server.utils.paddle_predictor import run_model
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__all__ = ['ASREngine']
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pretrained_models = {
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"deepspeech2online_aishell-zh-16k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.1.1.model.tar.gz',
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'md5':
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'd5e076217cf60486519f72c217d21b9b',
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'cfg_path':
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'model.yaml',
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'ckpt_path':
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'exp/deepspeech2_online/checkpoints/avg_1',
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'model':
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'exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel',
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'params':
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'exp/deepspeech2_online/checkpoints/avg_1.jit.pdiparams',
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'lm_url':
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'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
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'lm_md5':
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'29e02312deb2e59b3c8686c7966d4fe3'
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},
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}
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class ASRServerExecutor(ASRExecutor):
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def __init__(self):
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super().__init__()
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pass
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def _init_from_path(self,
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model_type: str='wenetspeech',
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am_model: Optional[os.PathLike]=None,
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am_params: Optional[os.PathLike]=None,
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lang: str='zh',
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sample_rate: int=16000,
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cfg_path: Optional[os.PathLike]=None,
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decode_method: str='attention_rescoring',
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am_predictor_conf: dict=None):
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"""
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Init model and other resources from a specific path.
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"""
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if cfg_path is None or am_model is None or am_params is None:
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sample_rate_str = '16k' if sample_rate == 16000 else '8k'
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tag = model_type + '-' + lang + '-' + sample_rate_str
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res_path = self._get_pretrained_path(tag) # wenetspeech_zh
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self.res_path = res_path
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self.cfg_path = os.path.join(res_path,
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pretrained_models[tag]['cfg_path'])
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self.am_model = os.path.join(res_path,
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pretrained_models[tag]['model'])
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self.am_params = os.path.join(res_path,
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pretrained_models[tag]['params'])
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logger.info(res_path)
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logger.info(self.cfg_path)
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logger.info(self.am_model)
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logger.info(self.am_params)
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.am_model = os.path.abspath(am_model)
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self.am_params = os.path.abspath(am_params)
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self.res_path = os.path.dirname(
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os.path.dirname(os.path.abspath(self.cfg_path)))
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#Init body.
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self.config = CfgNode(new_allowed=True)
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self.config.merge_from_file(self.cfg_path)
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with UpdateConfig(self.config):
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if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
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from paddlespeech.s2t.io.collator import SpeechCollator
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self.vocab = self.config.vocab_filepath
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self.config.decode.lang_model_path = os.path.join(
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MODEL_HOME, 'language_model',
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self.config.decode.lang_model_path)
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self.collate_fn_test = SpeechCollator.from_config(self.config)
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self.text_feature = TextFeaturizer(
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unit_type=self.config.unit_type, vocab=self.vocab)
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lm_url = pretrained_models[tag]['lm_url']
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lm_md5 = pretrained_models[tag]['lm_md5']
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self.download_lm(
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lm_url,
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os.path.dirname(self.config.decode.lang_model_path), lm_md5)
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elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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raise Exception("wrong type")
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else:
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raise Exception("wrong type")
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# AM predictor
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self.am_predictor_conf = am_predictor_conf
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self.am_predictor = init_predictor(
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model_file=self.am_model,
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params_file=self.am_params,
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predictor_conf=self.am_predictor_conf)
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# decoder
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self.decoder = CTCDecoder(
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odim=self.config.output_dim, # <blank> is in vocab
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enc_n_units=self.config.rnn_layer_size * 2,
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blank_id=self.config.blank_id,
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dropout_rate=0.0,
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reduction=True, # sum
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batch_average=True, # sum / batch_size
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grad_norm_type=self.config.get('ctc_grad_norm_type', None))
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# init decoder
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cfg = self.config.decode
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decode_batch_size = 1 # for online
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self.decoder.init_decoder(
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decode_batch_size, self.text_feature.vocab_list,
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cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta,
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cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
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cfg.num_proc_bsearch)
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# init state box
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self.chunk_state_h_box = np.zeros(
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(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
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dtype=float32)
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self.chunk_state_c_box = np.zeros(
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(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
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dtype=float32)
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def reset_decoder_and_chunk(self):
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"""reset decoder and chunk state for an new audio
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"""
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self.decoder.reset_decoder(batch_size=1)
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# init state box, for new audio request
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self.chunk_state_h_box = np.zeros(
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(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
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dtype=float32)
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self.chunk_state_c_box = np.zeros(
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(self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
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dtype=float32)
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def decode_one_chunk(self, x_chunk, x_chunk_lens, model_type: str):
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"""decode one chunk
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Args:
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x_chunk (numpy.array): shape[B, T, D]
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x_chunk_lens (numpy.array): shape[B]
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model_type (str): online model type
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Returns:
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[type]: [description]
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"""
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if "deepspeech2online" in model_type :
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input_names = self.am_predictor.get_input_names()
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audio_handle = self.am_predictor.get_input_handle(input_names[0])
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audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
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h_box_handle = self.am_predictor.get_input_handle(input_names[2])
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c_box_handle = self.am_predictor.get_input_handle(input_names[3])
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audio_handle.reshape(x_chunk.shape)
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audio_handle.copy_from_cpu(x_chunk)
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audio_len_handle.reshape(x_chunk_lens.shape)
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audio_len_handle.copy_from_cpu(x_chunk_lens)
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h_box_handle.reshape(self.chunk_state_h_box.shape)
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h_box_handle.copy_from_cpu(self.chunk_state_h_box)
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c_box_handle.reshape(self.chunk_state_c_box.shape)
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c_box_handle.copy_from_cpu(self.chunk_state_c_box)
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output_names = self.am_predictor.get_output_names()
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output_handle = self.am_predictor.get_output_handle(output_names[0])
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output_lens_handle = self.am_predictor.get_output_handle(output_names[1])
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output_state_h_handle = self.am_predictor.get_output_handle(
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output_names[2])
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output_state_c_handle = self.am_predictor.get_output_handle(
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output_names[3])
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self.am_predictor.run()
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output_chunk_probs = output_handle.copy_to_cpu()
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output_chunk_lens = output_lens_handle.copy_to_cpu()
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self.chunk_state_h_box = output_state_h_handle.copy_to_cpu()
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self.chunk_state_c_box = output_state_c_handle.copy_to_cpu()
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self.decoder.next(output_chunk_probs, output_chunk_lens)
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trans_best, trans_beam = self.decoder.decode()
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return trans_best[0]
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elif "conformer" in model_type or "transformer" in model_type:
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raise Exception("invalid model name")
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else:
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raise Exception("invalid model name")
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def _pcm16to32(self, audio):
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"""pcm int16 to float32
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Args:
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audio(numpy.array): numpy.int16
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Returns:
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audio(numpy.array): numpy.float32
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"""
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if audio.dtype == np.int16:
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audio = audio.astype("float32")
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bits = np.iinfo(np.int16).bits
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audio = audio / (2**(bits - 1))
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return audio
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def extract_feat(self, samples, sample_rate):
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"""extract feat
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Args:
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samples (numpy.array): numpy.float32
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sample_rate (int): sample rate
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Returns:
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x_chunk (numpy.array): shape[B, T, D]
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x_chunk_lens (numpy.array): shape[B]
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"""
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# pcm16 -> pcm 32
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samples = self._pcm16to32(samples)
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# read audio
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speech_segment = SpeechSegment.from_pcm(
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samples, sample_rate, transcript=" ")
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# audio augment
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self.collate_fn_test.augmentation.transform_audio(speech_segment)
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# extract speech feature
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spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize(
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speech_segment, self.collate_fn_test.keep_transcription_text)
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# CMVN spectrum
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if self.collate_fn_test._normalizer:
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spectrum = self.collate_fn_test._normalizer.apply(spectrum)
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# spectrum augment
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audio = self.collate_fn_test.augmentation.transform_feature(spectrum)
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audio_len = audio.shape[0]
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audio = paddle.to_tensor(audio, dtype='float32')
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# audio_len = paddle.to_tensor(audio_len)
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audio = paddle.unsqueeze(audio, axis=0)
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x_chunk = audio.numpy()
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x_chunk_lens = np.array([audio_len])
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return x_chunk, x_chunk_lens
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class ASREngine(BaseEngine):
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"""ASR server engine
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Args:
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metaclass: Defaults to Singleton.
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"""
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def __init__(self):
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super(ASREngine, self).__init__()
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def init(self, config: dict) -> bool:
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"""init engine resource
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Args:
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config_file (str): config file
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Returns:
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bool: init failed or success
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"""
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self.input = None
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self.output = ""
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self.executor = ASRServerExecutor()
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self.config = config
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self.executor._init_from_path(
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model_type=self.config.model_type,
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am_model=self.config.am_model,
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am_params=self.config.am_params,
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lang=self.config.lang,
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sample_rate=self.config.sample_rate,
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cfg_path=self.config.cfg_path,
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decode_method=self.config.decode_method,
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am_predictor_conf=self.config.am_predictor_conf)
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logger.info("Initialize ASR server engine successfully.")
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return True
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def preprocess(self, samples, sample_rate):
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"""preprocess
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Args:
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samples (numpy.array): numpy.float32
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sample_rate (int): sample rate
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Returns:
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x_chunk (numpy.array): shape[B, T, D]
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x_chunk_lens (numpy.array): shape[B]
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"""
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x_chunk, x_chunk_lens = self.executor.extract_feat(samples, sample_rate)
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return x_chunk, x_chunk_lens
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def run(self, x_chunk, x_chunk_lens, decoder_chunk_size=1):
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"""run online engine
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Args:
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x_chunk (numpy.array): shape[B, T, D]
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x_chunk_lens (numpy.array): shape[B]
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decoder_chunk_size(int)
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"""
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self.output = self.executor.decode_one_chunk(x_chunk, x_chunk_lens, self.config.model_type)
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def postprocess(self):
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|
|
"""postprocess
|
||
|
|
"""
|
||
|
|
return self.output
|
||
|
|
|
||
|
|
def reset(self):
|
||
|
|
"""reset engine decoder and inference state
|
||
|
|
"""
|
||
|
|
self.executor.reset_decoder_and_chunk()
|
||
|
|
self.output = ""
|